Training, Reskilling, Recruiting: The Future of Work in the Age of Generative AI
Anno: 2025
Autori/autrici: Marinelli, Luca; Cioli, Alessandra; Gregori, Gian Luca
Lingua: ENG
Classificazione IRIS: 2.1 Contributo in volume (Capitolo o Saggio)
Scheda completa IRIS: https://iris.univpm.it/handle/11566/348502
Citazioni:
Training, Reskilling, Recruiting: The Future of Work in the Age of Generative AI / Marinelli, Luca; Cioli, Alessandra; Gregori, Gian Luca. - (2025), pp. 237-256. [10.1108/978-1-83549-105-820251013]
Abstract:
This chapter examines the evolving landscape of workforce training and reskilling in response to the integration of Generative Artificial Intelligence (GenAI) in business processes. Employing a qualitative research design, the study draws insights from in-depth semi-structured interviews with a pane...l of experts, including human resources managers, headhunters, executives from training companies, and academia. The investigation centers on four key themes: (1) the emerging relationship between humans and GenAI; (2) workforce perceptions of GenAI; (3) the different training approaches adopted by companies; and (4) the skills required in the GenAI era. This chapter explores strategies used by organizations for upskilling - enhancing current competencies - and reskilling - acquiring entirely new skills, assessing whether these skills are developed in-house or sourced externally. Additionally, this chapter addresses how organizations communicate the necessity for adaptability, particularly in mitigating workforce concerns about AI-driven job displacement. The findings indicate that in-demand skills span technological proficiencies, cognitive, and interpersonal skills essential for maintaining competitiveness in the GenAI landscape. The study contributes to define the profile of the "worker of the future," providing practical insights for professionals navigating the GenAI revolution.
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Towards zero marine plastic pollution: a multidimensional measurement model for evaluating waste management solutions informed by SDGs 12 and 14
Anno: 2025
Rivista: MEASURING BUSINESS EXCELLENCE
Autori/autrici: Chiucchi, Maria Serena; Cioli, Alessandra; Guidi, Michele
Lingua: ENG
Classificazione IRIS: 1.1 Articolo in rivista
Scheda completa IRIS: https://iris.univpm.it/handle/11566/343773
Citazioni:
Towards zero marine plastic pollution: a multidimensional measurement model for evaluating waste management solutions informed by SDGs 12 and 14 / Chiucchi, Maria Serena; Cioli, Alessandra; Guidi, Michele. - In: MEASURING BUSINESS EXCELLENCE. - ISSN 1368-3047. - (2025). [Epub ahead of print] [10.1108/mbe-09-2024-0148]
Abstract:
Purpose: This paper explores how marine plastic waste management solutions can be holistically evaluated in alignment with Sustainable Development Goals (SDGs) 12 and 14. It aims to address the fragmentation in the existing literature by developing an integrated model for sustainability assessment t...hat combines cost-benefit analysis (CBA) and multi-criteria decision analysis (MCDA). Design/methodology/approach: Presenting an interventionist case study, the research examines a project aimed at collecting plastic waste from the sea and constructing a pyrolysis plant to convert non-recyclable plastics into energy in a major Italian port. A multidimensional evaluation model was developed through iterative stakeholder engagement, integrating financial and non-financial criteria across economic, environmental, and social dimensions to assess the investment. Data were gathered through semi-structured interviews, document analysis and field observations. Findings: The study introduces an integrated CBA–MCDA framework tailored to the complexities of sustainability-oriented investments. The model enables a comprehensive assessment of infrastructure projects by combining economic valuation with participatory evaluation of qualitative impacts. It also strengthens alignment with the targets of SDGs 12 and SDG 14 by incorporating criteria related to waste recovery, emissions reduction and social outcomes. Originality/value: This research contributes to the literature by operationalising the interdependence between SDGs 12 and 14 within a unified decision-making framework. It advances existing approaches by demonstrating how integrated methodologies can support investment decisions in contexts where environmental, economic, and social objectives converge, offering a replicable model for sustainability evaluation in waste management.
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Reconfiguring competitive advantage: a resource dynamic framework for generative AI adoption in digital content marketing
Anno: 2025
Rivista: EUROPEAN JOURNAL OF INNOVATION MANAGEMENT
Autori/autrici: Lanfranchi, Giuseppe; Cioli, Alessandra; Amanti, Andrea; Marinelli, Luca
Lingua: ENG
Classificazione IRIS: 1.1 Articolo in rivista
Scheda completa IRIS: https://iris.univpm.it/handle/11566/348499
Citazioni:
Reconfiguring competitive advantage: a resource dynamic framework for generative AI adoption in digital content marketing / Lanfranchi, Giuseppe; Cioli, Alessandra; Amanti, Andrea; Marinelli, Luca. - In: EUROPEAN JOURNAL OF INNOVATION MANAGEMENT. - ISSN 1460-1060. - (2025). [Epub ahead of print] [10.1108/ejim-03-2024-0317]
Abstract:
urpose – This multidisciplinary study investigates the transformative role of Generative Artificial Intelligence (Gen-AI) in the digital content marketing (DCM) domain through the lens of the Resource-Based View (RBV). The research examines Gen-AI’s impact on DCM strategies and practices..., including ethical, legal, and professional implications, thereby highlighting the interplay between rapidly accessible AI solutions and distinctive internal assets that are difficult for competitors to replicate. Design/methodology/approach – A Delphi method was employed through a panel of 14 experts to formulate consensus-based recommendations for stakeholders in key areas of content marketing, covering Gen-AI’s benefits, content quality and creativity, DCM strategies, ethical and legal considerations, and future technological developments. This expert-based process was combined with an RBV-oriented theoretical framework and a critical review of extant literature, providing a structured approach to capturing how Gen-AI adoption intersects with an organization’s unique resources and capabilities. Findings – The findings underscore Gen-AI’s advantages in augmenting efficiency, innovation, and customization in content creation, emphasizing the growing need for AI-focused skill development, particularly “prompt engineering.” However, the real source of competitive advantage emerges not merely from adopting new technologies, but from integrating them with proprietary data, specialized know-how, and a culture of innovation. The RBV framework elucidates how these intangible resources, when effectively harnessed and preserved in a dynamic context, foster strategies capable of sustaining a durable competitive edge in DCM. Originality/value – The study contributes to both theory and practice by merging the insights of the RBV with the evolving landscape of Gen-AI in DCM. It is among the first to provide a comprehensive framework that illustrates how AI-centric innovations, combined with hard-to-imitate organizational assets, can reinforce long-term strategic benefits. The recommendations derived from the Delphi process offer valuable guidance for stakeholders seeking to navigate the Gen-AI landscape responsibly, ensuring that ethical and legal considerations remain central to a robust and future-oriented DCM strategy.
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